Overview

Dataset statistics

Number of variables11
Number of observations94881
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.0 MiB
Average record size in memory88.0 B

Variable types

Categorical1
DateTime1
Numeric9

Warnings

id_estacion has a high cardinality: 207 distinct values High cardinality
tmax is highly correlated with tminHigh correlation
tmin is highly correlated with tmaxHigh correlation
longitud is highly correlated with latitudHigh correlation
latitud is highly correlated with longitudHigh correlation
tmax is highly correlated with tminHigh correlation
tmin is highly correlated with tmaxHigh correlation
tmax is highly correlated with tminHigh correlation
tmin is highly correlated with tmaxHigh correlation
longitud is highly correlated with latitud and 1 other fieldsHigh correlation
latitud is highly correlated with longitud and 1 other fieldsHigh correlation
tmin is highly correlated with altitud and 2 other fieldsHigh correlation
altitud is highly correlated with longitud and 3 other fieldsHigh correlation
fecha_cnt is highly correlated with tmin and 1 other fieldsHigh correlation
tmax is highly correlated with tmin and 2 other fieldsHigh correlation
nevada is highly skewed (γ1 = 133.5740724) Skewed
prof_nieve is highly skewed (γ1 = 62.17306945) Skewed
precip has 13169 (13.9%) zeros Zeros
nevada has 94869 (> 99.9%) zeros Zeros
prof_nieve has 93937 (99.0%) zeros Zeros

Reproduction

Analysis started2021-10-09 13:00:21.422362
Analysis finished2021-10-09 13:00:33.105205
Duration11.68 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

id_estacion
Categorical

HIGH CARDINALITY

Distinct207
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size741.4 KiB
SP000009981
 
1391
SP000008280
 
1329
SP000003195
 
1218
SPE00120629
 
1210
SPE00155259
 
1206
Other values (202)
88527 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters1043691
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSP000003195
2nd rowSP000003195
3rd rowSP000003195
4th rowSP000003195
5th rowSP000003195

Common Values

ValueCountFrequency (%)
SP0000099811391
 
1.5%
SP0000082801329
 
1.4%
SP0000031951218
 
1.3%
SPE001206291210
 
1.3%
SPE001552591206
 
1.3%
SP0000600101198
 
1.3%
SP0000080271130
 
1.2%
SP0000070381099
 
1.2%
SPE001197111090
 
1.1%
SPE001204581088
 
1.1%
Other values (197)82922
87.4%

Length

2021-10-09T13:00:33.343372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp0000099811391
 
1.5%
sp0000082801329
 
1.4%
sp0000031951218
 
1.3%
spe001206291210
 
1.3%
spe001552591206
 
1.3%
sp0000600101198
 
1.3%
sp0000080271130
 
1.2%
sp0000070381099
 
1.2%
spe001197111090
 
1.1%
spe001204581088
 
1.1%
Other values (197)82922
87.4%

Most occurring characters

ValueCountFrequency (%)
0319330
30.6%
1131987
12.6%
S94881
 
9.1%
P94881
 
9.1%
279522
 
7.6%
E76032
 
7.3%
547507
 
4.6%
947023
 
4.5%
634715
 
3.3%
833383
 
3.2%
Other values (5)84430
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number774846
74.2%
Uppercase Letter268845
 
25.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0319330
41.2%
1131987
17.0%
279522
 
10.3%
547507
 
6.1%
947023
 
6.1%
634715
 
4.5%
833383
 
4.3%
331430
 
4.1%
429670
 
3.8%
720279
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
S94881
35.3%
P94881
35.3%
E76032
28.3%
W1903
 
0.7%
M1148
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common774846
74.2%
Latin268845
 
25.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0319330
41.2%
1131987
17.0%
279522
 
10.3%
547507
 
6.1%
947023
 
6.1%
634715
 
4.5%
833383
 
4.3%
331430
 
4.1%
429670
 
3.8%
720279
 
2.6%
Latin
ValueCountFrequency (%)
S94881
35.3%
P94881
35.3%
E76032
28.3%
W1903
 
0.7%
M1148
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1043691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0319330
30.6%
1131987
12.6%
S94881
 
9.1%
P94881
 
9.1%
279522
 
7.6%
E76032
 
7.3%
547507
 
4.6%
947023
 
4.5%
634715
 
3.3%
833383
 
3.2%
Other values (5)84430
 
8.1%

fecha
Date

Distinct1465
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size741.4 KiB
Minimum1896-11-30 00:00:00
Maximum2021-08-31 00:00:00
2021-10-09T13:00:33.449454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:00:33.563333image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

fecha_cnt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.496759098
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size741.4 KiB
2021-10-09T13:00:33.779094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.447215885
Coefficient of variation (CV)0.5306054654
Kurtosis-1.212344505
Mean6.496759098
Median Absolute Deviation (MAD)3
Skewness0.00209677699
Sum616419
Variance11.88329736
MonotonicityNot monotonic
2021-10-09T13:00:33.862335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
37966
8.4%
57964
8.4%
47956
8.4%
67936
8.4%
77935
8.4%
17927
8.4%
87924
8.4%
97883
8.3%
107878
8.3%
127878
8.3%
Other values (2)15634
16.5%
ValueCountFrequency (%)
17927
8.4%
27760
8.2%
37966
8.4%
47956
8.4%
57964
8.4%
67936
8.4%
77935
8.4%
87924
8.4%
97883
8.3%
107878
8.3%
ValueCountFrequency (%)
127878
8.3%
117874
8.3%
107878
8.3%
97883
8.3%
87924
8.4%
77935
8.4%
67936
8.4%
57964
8.4%
47956
8.4%
37966
8.4%

tmax
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct447
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.2001876
Minimum-53
Maximum403
Zeros14
Zeros (%)< 0.1%
Negative236
Negative (%)0.2%
Memory size741.4 KiB
2021-10-09T13:00:34.054279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-53
5-th percentile88
Q1148
median198
Q3255
95-th percentile316
Maximum403
Range456
Interquartile range (IQR)107

Descriptive statistics

Standard deviation71.25637968
Coefficient of variation (CV)0.3559256389
Kurtosis-0.5046758729
Mean200.2001876
Median Absolute Deviation (MAD)53
Skewness-0.02638990299
Sum18995194
Variance5077.471645
MonotonicityNot monotonic
2021-10-09T13:00:34.227704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
166547
 
0.6%
171543
 
0.6%
164535
 
0.6%
154529
 
0.6%
178519
 
0.5%
158515
 
0.5%
174514
 
0.5%
167510
 
0.5%
172507
 
0.5%
180505
 
0.5%
Other values (437)89657
94.5%
ValueCountFrequency (%)
-531
 
< 0.1%
-501
 
< 0.1%
-491
 
< 0.1%
-471
 
< 0.1%
-461
 
< 0.1%
-451
 
< 0.1%
-441
 
< 0.1%
-431
 
< 0.1%
-421
 
< 0.1%
-413
< 0.1%
ValueCountFrequency (%)
4031
 
< 0.1%
4011
 
< 0.1%
4001
 
< 0.1%
3981
 
< 0.1%
3961
 
< 0.1%
3952
< 0.1%
3911
 
< 0.1%
3892
< 0.1%
3884
< 0.1%
3871
 
< 0.1%

tmin
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct366
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.85705252
Minimum-121
Maximum254
Zeros197
Zeros (%)0.2%
Negative5003
Negative (%)5.3%
Memory size741.4 KiB
2021-10-09T13:00:34.376728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-121
5-th percentile-2
Q153
median98
Q3148
95-th percentile199
Maximum254
Range375
Interquartile range (IQR)95

Descriptive statistics

Standard deviation62.26292305
Coefficient of variation (CV)0.6298278319
Kurtosis-0.6619987427
Mean98.85705252
Median Absolute Deviation (MAD)48
Skewness-0.07068337551
Sum9379656
Variance3876.671587
MonotonicityNot monotonic
2021-10-09T13:00:34.497894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86569
 
0.6%
99563
 
0.6%
81561
 
0.6%
74561
 
0.6%
80558
 
0.6%
66555
 
0.6%
82554
 
0.6%
84553
 
0.6%
77551
 
0.6%
75550
 
0.6%
Other values (356)89306
94.1%
ValueCountFrequency (%)
-1211
 
< 0.1%
-1151
 
< 0.1%
-1141
 
< 0.1%
-1133
< 0.1%
-1123
< 0.1%
-1102
< 0.1%
-1092
< 0.1%
-1082
< 0.1%
-1062
< 0.1%
-1051
 
< 0.1%
ValueCountFrequency (%)
2543
< 0.1%
2521
 
< 0.1%
2514
< 0.1%
2504
< 0.1%
2491
 
< 0.1%
2482
< 0.1%
2471
 
< 0.1%
2463
< 0.1%
2453
< 0.1%
2444
< 0.1%

precip
Real number (ℝ≥0)

ZEROS

Distinct223
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.24936499
Minimum0
Maximum422
Zeros13169
Zeros (%)13.9%
Negative0
Negative (%)0.0%
Memory size741.4 KiB
2021-10-09T13:00:34.607674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median10
Q322
95-th percentile53
Maximum422
Range422
Interquartile range (IQR)19

Descriptive statistics

Standard deviation19.80259663
Coefficient of variation (CV)1.218668953
Kurtosis17.20503659
Mean16.24936499
Median Absolute Deviation (MAD)9
Skewness2.939634725
Sum1541756
Variance392.1428333
MonotonicityNot monotonic
2021-10-09T13:00:34.711754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013169
 
13.9%
15127
 
5.4%
24310
 
4.5%
33800
 
4.0%
43650
 
3.8%
53340
 
3.5%
63214
 
3.4%
73016
 
3.2%
82919
 
3.1%
92819
 
3.0%
Other values (213)49517
52.2%
ValueCountFrequency (%)
013169
13.9%
15127
 
5.4%
24310
 
4.5%
33800
 
4.0%
43650
 
3.8%
53340
 
3.5%
63214
 
3.4%
73016
 
3.2%
82919
 
3.1%
92819
 
3.0%
ValueCountFrequency (%)
4221
< 0.1%
3711
< 0.1%
3201
< 0.1%
3091
< 0.1%
3051
< 0.1%
2991
< 0.1%
2801
< 0.1%
2791
< 0.1%
2591
< 0.1%
2521
< 0.1%

nevada
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0002951065018
Minimum0
Maximum6
Zeros94869
Zeros (%)> 99.9%
Negative0
Negative (%)0.0%
Memory size741.4 KiB
2021-10-09T13:00:34.795613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.03045324718
Coefficient of variation (CV)103.1940909
Kurtosis21217.72797
Mean0.0002951065018
Median Absolute Deviation (MAD)0
Skewness133.5740724
Sum28
Variance0.0009274002637
MonotonicityNot monotonic
2021-10-09T13:00:34.861584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
094869
> 99.9%
26
 
< 0.1%
13
 
< 0.1%
41
 
< 0.1%
31
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
094869
> 99.9%
13
 
< 0.1%
26
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
61
 
< 0.1%
41
 
< 0.1%
31
 
< 0.1%
26
 
< 0.1%
13
 
< 0.1%
094869
> 99.9%

prof_nieve
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct170
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4669533416
Minimum0
Maximum1834
Zeros93937
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size741.4 KiB
2021-10-09T13:00:34.955724image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1834
Range1834
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14.85144419
Coefficient of variation (CV)31.80498536
Kurtosis5226.968602
Mean0.4669533416
Median Absolute Deviation (MAD)0
Skewness62.17306945
Sum44305
Variance220.5653945
MonotonicityNot monotonic
2021-10-09T13:00:35.064034image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
093937
99.0%
1302
 
0.3%
2123
 
0.1%
367
 
0.1%
443
 
< 0.1%
530
 
< 0.1%
618
 
< 0.1%
717
 
< 0.1%
1110
 
< 0.1%
159
 
< 0.1%
Other values (160)325
 
0.3%
ValueCountFrequency (%)
093937
99.0%
1302
 
0.3%
2123
 
0.1%
367
 
0.1%
443
 
< 0.1%
530
 
< 0.1%
618
 
< 0.1%
717
 
< 0.1%
88
 
< 0.1%
94
 
< 0.1%
ValueCountFrequency (%)
18341
< 0.1%
14941
< 0.1%
11681
< 0.1%
10731
< 0.1%
10171
< 0.1%
8921
< 0.1%
7871
< 0.1%
7841
< 0.1%
7091
< 0.1%
6661
< 0.1%

longitud
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct201
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.66353835
Minimum27.8189
Maximum43.5667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size741.4 KiB
2021-10-09T13:00:35.174644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum27.8189
5-th percentile28.4775
Q138.282
median40.8206
Q342.0831
95-th percentile43.3669
Maximum43.5667
Range15.7478
Interquartile range (IQR)3.8011

Descriptive statistics

Standard deviation3.767045748
Coefficient of variation (CV)0.09497503009
Kurtosis3.134861779
Mean39.66353835
Median Absolute Deviation (MAD)1.6197
Skewness-1.844457219
Sum3763316.182
Variance14.19063367
MonotonicityNot monotonic
2021-10-09T13:00:35.284780image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.82061391
 
1.5%
38.95191329
 
1.4%
40.41171218
 
1.3%
41.11441210
 
1.3%
41.41811206
 
1.3%
28.30891198
 
1.3%
38.98921192
 
1.3%
40.94781155
 
1.2%
43.30751130
 
1.2%
37.97691099
 
1.2%
Other values (191)82753
87.2%
ValueCountFrequency (%)
27.8189573
0.6%
27.9225633
0.7%
28.0475493
0.5%
28.30891198
1.3%
28.4444665
0.7%
28.46311088
1.1%
28.4775951
1.0%
28.6331654
0.7%
28.9517640
0.7%
35.2778715
0.8%
ValueCountFrequency (%)
43.5667634
0.7%
43.5606524
0.6%
43.5381750
0.8%
43.4917613
0.6%
43.4644878
0.9%
43.4292708
0.7%
43.36691090
1.1%
43.3606764
0.8%
43.3542585
0.6%
43.30751130
1.2%

latitud
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct206
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.4350247
Minimum-17.8889
Maximum4.2156
Zeros0
Zeros (%)0.0%
Negative66473
Negative (%)70.1%
Memory size741.4 KiB
2021-10-09T13:00:35.414869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-17.8889
5-th percentile-16.2553
Q1-5.6417
median-3.45
Q30.4914
95-th percentile2.3767
Maximum4.2156
Range22.1045
Interquartile range (IQR)6.1331

Descriptive statistics

Standard deviation4.699469291
Coefficient of variation (CV)-1.368103493
Kurtosis1.512397182
Mean-3.4350247
Median Absolute Deviation (MAD)2.6056
Skewness-1.173117137
Sum-325918.5786
Variance22.08501162
MonotonicityNot monotonic
2021-10-09T13:00:35.529559image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.78921506
 
1.6%
0.49141391
 
1.5%
-1.86311329
 
1.4%
-3.67811218
 
1.3%
-1.41061210
 
1.3%
2.12391206
 
1.3%
-16.49921198
 
1.3%
-2.03921130
 
1.2%
0.71061099
 
1.2%
-8.41921090
 
1.1%
Other values (196)82504
87.0%
ValueCountFrequency (%)
-17.8889573
0.6%
-17.755654
0.7%
-16.5606493
0.5%
-16.49921198
1.3%
-16.3292951
1.0%
-16.25531088
1.1%
-15.3892633
0.7%
-13.8631665
0.7%
-13.6003640
0.7%
-8.6494320
 
0.3%
ValueCountFrequency (%)
4.2156652
0.7%
3.1817156
 
0.2%
3.1658156
 
0.2%
3.0967156
 
0.2%
3.0353156
 
0.2%
3.0325156
 
0.2%
2.834260
 
0.1%
2.8267132
 
0.1%
2.8253695
0.7%
2.8067124
 
0.1%

altitud
Real number (ℝ≥0)

HIGH CORRELATION

Distinct173
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean418.5396844
Minimum1
Maximum2535
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size741.4 KiB
2021-10-09T13:00:35.646250image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q142
median247
Q3656
95-th percentile1143
Maximum2535
Range2534
Interquartile range (IQR)614

Descriptive statistics

Standard deviation504.207495
Coefficient of variation (CV)1.204682647
Kurtosis4.649018778
Mean418.5396844
Median Absolute Deviation (MAD)230
Skewness1.9670506
Sum39711463.8
Variance254225.1981
MonotonicityNot monotonic
2021-10-09T13:00:35.793016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42794
 
2.9%
12016
 
2.1%
351672
 
1.8%
321616
 
1.7%
441391
 
1.5%
51375
 
1.4%
641371
 
1.4%
871345
 
1.4%
7041329
 
1.4%
251305
 
1.4%
Other values (163)78667
82.9%
ValueCountFrequency (%)
12016
2.1%
2312
 
0.3%
3742
 
0.8%
42794
2.9%
51375
1.4%
6644
 
0.7%
71300
1.4%
8118
 
0.1%
111033
 
1.1%
14796
 
0.8%
ValueCountFrequency (%)
2535156
 
0.2%
2519155
 
0.2%
2451156
 
0.2%
2400155
 
0.2%
23711198
1.3%
2316156
 
0.2%
2266156
 
0.2%
2247156
 
0.2%
2230156
 
0.2%
2228156
 
0.2%

Interactions

2021-10-09T13:00:24.137743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-09T13:00:24.708364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-09T13:00:24.804521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

2021-10-09T13:00:35.907156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-09T13:00:36.045206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-09T13:00:36.178709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's Ļ„

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (Ļ„) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate Ļ„ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. Ļ„ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-09T13:00:36.316530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-09T13:00:32.601726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-09T13:00:32.902245image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

id_estacionfechafecha_cnttmaxtminprecipnevadaprof_nievelongitudlatitudaltitud
0SP0000031951920-01-311103.022.01.00.00.040.4117-3.6781667.0
1SP0000031951920-02-292119.037.031.00.00.040.4117-3.6781667.0
2SP0000031951920-03-313149.050.010.00.00.040.4117-3.6781667.0
3SP0000031951920-04-304189.076.09.00.00.040.4117-3.6781667.0
4SP0000031951920-05-315250.0120.024.00.00.040.4117-3.6781667.0
5SP0000031951920-06-306274.0149.05.00.00.040.4117-3.6781667.0
6SP0000031951920-07-317311.0165.02.00.00.040.4117-3.6781667.0
7SP0000031951920-08-318311.0168.00.00.00.040.4117-3.6781667.0
8SP0000031951920-09-309254.0134.01.00.00.040.4117-3.6781667.0
9SP0000031951920-10-3110160.084.028.00.00.040.4117-3.6781667.0

Last rows

id_estacionfechafecha_cnttmaxtminprecipnevadaprof_nievelongitudlatitudaltitud
94871SPW000140111967-03-313172.029.05.00.00.040.4833-3.45608.1
94872SPW000140111967-04-304164.038.015.00.00.040.4833-3.45608.1
94873SPW000140111967-05-315194.061.012.00.00.040.4833-3.45608.1
94874SPW000140111967-06-306252.0101.04.00.00.040.4833-3.45608.1
94875SPW000140111967-07-317345.0154.00.00.00.040.4833-3.45608.1
94876SPW000140111967-08-318322.0141.00.00.00.040.4833-3.45608.1
94877SPW000140111967-09-309257.0102.05.00.00.040.4833-3.45608.1
94878SPW000140111967-10-3110213.090.017.00.00.040.4833-3.45608.1
94879SPW000140111967-11-3011131.044.029.00.00.040.4833-3.45608.1
94880SPW000140111967-12-311282.0-23.00.00.00.040.4833-3.45608.1